基于深度三元组网络驱动的异步航迹关联算法OA

To solve the problem that current track-to-track association(T2TA)algorithms cannot distinguish similar tracks in dense scenarios,an asynchronous T2TA method based on deep triplet network is proposed.This algorithm is a two-stage method.In the first stage,on the basis of extracting temporal features by long short-term memory(LSTM),this algorithm utilizes deep metric learning with triplet loss to map tracks into a metric space.It can effectively increase the feature distance between positive and negative samples,thereby distinguishing the subtle feature of similar target.In the second stage,the association problem is taken as a binary classification task,and the track features are effectively classified by a binary classifier.Simulation experiments show that in dense target environment,the proposed method achieves an average correct association rate of 91.6%,which is 6.9%higher than that of traditional deep learning method.